





"""simple docstring"""






from __future__ import annotations

import unittest

from transformers import AutoTokenizer, PegasusConfig, is_tf_available
from transformers.testing_utils import require_sentencepiece, require_tf, require_tokenizers, slow
from transformers.utils import cached_property

from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_tf_available():
		import tensorflow as tf

		from transformers import TFAutoModelForSeqaSeqLM, TFPegasusForConditionalGeneration, TFPegasusModel




@require_tf
class 					__lowercase      :







					'''simple docstring'''




					__lowerCAmelCase	=	PegasusConfig
					__lowerCAmelCase	=	{}
					__lowerCAmelCase	=	'''gelu'''







					def __init__(    self						,      _UpperCAmelCase						,      _UpperCAmelCase=13						,      _UpperCAmelCase=7						,      _UpperCAmelCase=True						,      _UpperCAmelCase=False						,      _UpperCAmelCase=99						,      _UpperCAmelCase=32						,      _UpperCAmelCase=2						,      _UpperCAmelCase=4						,      _UpperCAmelCase=37						,      _UpperCAmelCase=0.1						,      _UpperCAmelCase=0.1						,      _UpperCAmelCase=40						,      _UpperCAmelCase=2						,      _UpperCAmelCase=1						,      _UpperCAmelCase=0						,      ):
									__a			:    Union[str, Any]      							=	parent
									__a			:    Tuple      							=	batch_size
									__a			:    Tuple      							=	seq_length
									__a			:    List[Any]      							=	is_training
									__a			:    Optional[int]      							=	use_labels
									__a			:    int      							=	vocab_size
									__a			:    Optional[Any]      							=	hidden_size
									__a			:    Union[str, Any]      							=	num_hidden_layers
									__a			:    Union[str, Any]      							=	num_attention_heads
									__a			:    Union[str, Any]      							=	intermediate_size

									__a			:    Tuple      							=	hidden_dropout_prob
									__a			:    int      							=	attention_probs_dropout_prob
									__a			:    Tuple      							=	max_position_embeddings
									__a			:    List[Any]      							=	eos_token_id
									__a			:    Optional[int]      							=	pad_token_id
									__a			:    Optional[int]      							=	bos_token_id







					def 			_lowerCamelCase		(    self  ):
									__a			:    Dict      							=	ids_tensor([self.batch_size, self.seq_length - 1]						,      self.vocab_size  )
									__a			:    Union[str, Any]      							=	tf.expand_dims(tf.constant([self.eos_token_id] * self.batch_size  )						,      1  )
									__a			:    Dict      							=	tf.concat([input_ids, eos_tensor]						,      axis=1  )

									__a			:    List[Any]      							=	ids_tensor([self.batch_size, self.seq_length]						,      self.vocab_size  )

									__a			:    Optional[int]      							=	self.config_cls(
									    vocab_size=self.vocab_size						,      d_model=self.hidden_size						,      encoder_layers=self.num_hidden_layers						,      decoder_layers=self.num_hidden_layers						,      encoder_attention_heads=self.num_attention_heads						,      decoder_attention_heads=self.num_attention_heads						,      encoder_ffn_dim=self.intermediate_size						,      decoder_ffn_dim=self.intermediate_size						,      dropout=self.hidden_dropout_prob						,      attention_dropout=self.attention_probs_dropout_prob						,      max_position_embeddings=self.max_position_embeddings						,      eos_token_ids=[2]						,      bos_token_id=self.bos_token_id						,      pad_token_id=self.pad_token_id						,      decoder_start_token_id=self.pad_token_id						,      **self.config_updates						,      )
									__a			:    Optional[Any]      							=	prepare_pegasus_inputs_dict(_UpperCAmelCase						,      _UpperCAmelCase						,      _UpperCAmelCase  )
									return config, inputs_dict







					def 			_lowerCamelCase		(    self						,      _UpperCAmelCase						,      _UpperCAmelCase  ):
									__a			:    Dict      							=	TFPegasusModel(config=_UpperCAmelCase  ).get_decoder()
									__a			:    int      							=	inputs_dict['''input_ids''']

									__a			:    List[str]      							=	input_ids[:1, :]
									__a			:    List[str]      							=	inputs_dict['''attention_mask'''][:1, :]
									__a			:    Optional[int]      							=	inputs_dict['''head_mask''']
									__a			:    List[Any]      							=	1

									# first forward pass
									__a			:    Union[str, Any]      							=	model(_UpperCAmelCase						,      attention_mask=_UpperCAmelCase						,      head_mask=_UpperCAmelCase						,      use_cache=_UpperCAmelCase  )

									__a							,    __a			:    Tuple      							=	outputs.to_tuple()

									# create hypothetical next token and extent to next_input_ids
									__a			:    List[Any]      							=	ids_tensor((self.batch_size, 3)						,      config.vocab_size  )
									__a			:    int      							=	tf.cast(ids_tensor((self.batch_size, 3)						,      2  )						,      tf.inta  )

									# append to next input_ids and
									__a			:    Optional[Any]      							=	tf.concat([input_ids, next_tokens]						,      axis=-1  )
									__a			:    Dict      							=	tf.concat([attention_mask, next_attn_mask]						,      axis=-1  )

									__a			:    Tuple      							=	model(_UpperCAmelCase						,      attention_mask=_UpperCAmelCase  )[0]
									__a			:    Union[str, Any]      							=	model(_UpperCAmelCase						,      attention_mask=_UpperCAmelCase						,      past_key_values=_UpperCAmelCase  )[0]

									self.parent.assertEqual(next_tokens.shape[1]						,      output_from_past.shape[1]  )

									# select random slice
									__a			:    Optional[Any]      							=	int(ids_tensor((1,)						,      output_from_past.shape[-1]  )  )
									__a			:    Union[str, Any]      							=	output_from_no_past[:, -3:, random_slice_idx]
									__a			:    str      							=	output_from_past[:, :, random_slice_idx]

									# test that outputs are equal for slice
									tf.debugging.assert_near(_UpperCAmelCase						,      _UpperCAmelCase						,      rtol=1e-3  )






def      __A   (      a_    :List[Any] ,							a_    :str ,							a_    :int ,							a_    :Union[str, Any]=None ,							a_    :Optional[Any]=None ,							a_    :Union[str, Any]=None ,							a_    :List[Any]=None ,							a_    :List[str]=None ,							)		->		Dict:
				if attention_mask is None:
								__a			:    Dict      							=	tf.cast(tf.math.not_equal(a_ ,							config.pad_token_id) ,							tf.inta)
				if decoder_attention_mask is None:
								__a			:    Optional[Any]      							=	tf.concat(
								    [
								        tf.ones(decoder_input_ids[:, :1].shape ,							dtype=tf.inta),
								        tf.cast(tf.math.not_equal(decoder_input_ids[:, 1:] ,							config.pad_token_id) ,							tf.inta),
								    ] ,							axis=-1 ,							)
				if head_mask is None:
								__a			:    Optional[int]      							=	tf.ones((config.encoder_layers, config.encoder_attention_heads))
				if decoder_head_mask is None:
								__a			:    Union[str, Any]      							=	tf.ones((config.decoder_layers, config.decoder_attention_heads))
				if cross_attn_head_mask is None:
								__a			:    Optional[Any]      							=	tf.ones((config.decoder_layers, config.decoder_attention_heads))
				return {
				    "input_ids": input_ids,
				    "decoder_input_ids": decoder_input_ids,
				    "attention_mask": attention_mask,
				    "decoder_attention_mask": decoder_attention_mask,
				    "head_mask": head_mask,
				    "decoder_head_mask": decoder_head_mask,
				    "cross_attn_head_mask": cross_attn_head_mask,
				}




@require_tf
class 					__lowercase      (      _UpperCamelCase ,   _UpperCamelCase ,   unittest.TestCase				):







					'''simple docstring'''




					__lowerCAmelCase	=	(TFPegasusForConditionalGeneration, TFPegasusModel) if is_tf_available() else ()
					__lowerCAmelCase	=	(TFPegasusForConditionalGeneration,) if is_tf_available() else ()
					__lowerCAmelCase	=	(
					    {
					        '''conversational''': TFPegasusForConditionalGeneration,
					        '''feature-extraction''': TFPegasusModel,
					        '''summarization''': TFPegasusForConditionalGeneration,
					        '''text2text-generation''': TFPegasusForConditionalGeneration,
					        '''translation''': TFPegasusForConditionalGeneration,
					    }
					    if is_tf_available()
					    else {}
					)
					__lowerCAmelCase	=	True
					__lowerCAmelCase	=	False
					__lowerCAmelCase	=	False







					def 			_lowerCamelCase		(    self  ):
									__a			:    List[str]      							=	TFPegasusModelTester(self  )
									__a			:    int      							=	ConfigTester(self						,      config_class=_UpperCAmelCase  )







					def 			_lowerCamelCase		(    self  ):
									self.config_tester.run_common_tests()







					def 			_lowerCamelCase		(    self  ):
									__a			:    Dict      							=	self.model_tester.prepare_config_and_inputs_for_common()
									self.model_tester.check_decoder_model_past_large_inputs(*_UpperCAmelCase  )






@require_sentencepiece
@require_tokenizers
@require_tf
class 					__lowercase      (      unittest.TestCase				):







					'''simple docstring'''




					__lowerCAmelCase	=	[
					    ''' PG&E stated it scheduled the blackouts in response to forecasts for high winds amid dry conditions. The aim is to reduce the risk of wildfires. Nearly 800 thousand customers were scheduled to be affected by the shutoffs which were expected to last through at least midday tomorrow.''',
					    ''' The London trio are up for best UK act and best album, as well as getting two nominations in the best song category."We got told like this morning \'Oh I think you\'re nominated\'", said Dappy."And I was like \'Oh yeah, which one?\' And now we\'ve got nominated for four awards. I mean, wow!"Bandmate Fazer added: "We thought it\'s best of us to come down and mingle with everyone and say hello to the cameras. And now we find we\'ve got four nominations."The band have two shots at the best song prize, getting the nod for their Tynchy Stryder collaboration Number One, and single Strong Again.Their album Uncle B will also go up against records by the likes of Beyonce and Kanye West.N-Dubz picked up the best newcomer Mobo in 2007, but female member Tulisa said they wouldn\'t be too disappointed if they didn\'t win this time around."At the end of the day we\'re grateful to be where we are in our careers."If it don\'t happen then it don\'t happen - live to fight another day and keep on making albums and hits for the fans."Dappy also revealed they could be performing live several times on the night.The group will be doing Number One and also a possible rendition of the War Child single, I Got Soul.The charity song is a  re-working of The Killers\' All These Things That I\'ve Done and is set to feature artists like Chipmunk, Ironik and Pixie Lott.This year\'s Mobos will be held outside of London for the first time, in Glasgow on 30 September.N-Dubz said they were looking forward to performing for their Scottish fans and boasted about their recent shows north of the border."We just done Edinburgh the other day," said Dappy."We smashed up an N-Dubz show over there. We done Aberdeen about three or four months ago - we smashed up that show over there! Everywhere we go we smash it up!" ''',
					]
					__lowerCAmelCase	=	[
					    '''California\'s largest electricity provider has cut power to hundreds of thousands of customers in an effort to'''
					    ''' reduce the risk of wildfires.''',
					    '''N-Dubz have revealed they\'re "grateful" to have been nominated for four Mobo Awards.''',
					]  # differs slightly from pytorch, likely due to numerical differences in linear layers
					__lowerCAmelCase	=	'''google/pegasus-xsum'''







					@cached_property
					def 			_lowerCamelCase		(    self  ):
									return AutoTokenizer.from_pretrained(self.model_name  )







					@cached_property
					def 			_lowerCamelCase		(    self  ):
									__a			:    Any      							=	TFAutoModelForSeqaSeqLM.from_pretrained(self.model_name  )
									return model







					def 			_lowerCamelCase		(    self						,      **_UpperCAmelCase  ):
									__a			:    str      							=	self.translate_src_text(**_UpperCAmelCase  )
									assert self.expected_text == generated_words







					def 			_lowerCamelCase		(    self						,      **_UpperCAmelCase  ):
									__a			:    List[Any]      							=	self.tokenizer(self.src_text						,      **_UpperCAmelCase						,      padding=_UpperCAmelCase						,      return_tensors='''tf'''  )
									__a			:    Tuple      							=	self.model.generate(
									    model_inputs.input_ids						,      attention_mask=model_inputs.attention_mask						,      num_beams=2						,      use_cache=_UpperCAmelCase						,      )
									__a			:    Any      							=	self.tokenizer.batch_decode(generated_ids.numpy()						,      skip_special_tokens=_UpperCAmelCase  )
									return generated_words







					@slow
					def 			_lowerCamelCase		(    self  ):
									self._assert_generated_batch_equal_expected()







'''simple docstring'''







import argparse

import numpy as np
import torch

from transformers import SpeechTaHifiGan, SpeechTaHifiGanConfig, logging


logging.set_verbosity_info()
__lowerCAmelCase        =							logging.get_logger("transformers.models.speecht5")


def       __UpperCamelCase					(      lowercase_       :  Optional[int]   ,       lowercase_       :  Dict   ,       lowercase_       :  Any   ):
       """simple docstring"""




       hf_model.apply_weight_norm()

       a_						     =			checkpoint['input_conv.weight_g']
       a_						     =			checkpoint['input_conv.weight_v']
       a_						     =			checkpoint['input_conv.bias']

       for i in range(len(config.upsample_rates   )   ):
              a_						     =			checkpoint[F'upsamples.{i}.1.weight_g']
              a_						     =			checkpoint[F'upsamples.{i}.1.weight_v']
              a_						     =			checkpoint[F'upsamples.{i}.1.bias']

       for i in range(len(config.upsample_rates   ) * len(config.resblock_kernel_sizes   )   ):
              for j in range(len(config.resblock_dilation_sizes   )   ):
                     a_						     =			checkpoint[F'blocks.{i}.convs1.{j}.1.weight_g']
                     a_						     =			checkpoint[F'blocks.{i}.convs1.{j}.1.weight_v']
                     a_						     =			checkpoint[F'blocks.{i}.convs1.{j}.1.bias']

                     a_						     =			checkpoint[F'blocks.{i}.convs2.{j}.1.weight_g']
                     a_						     =			checkpoint[F'blocks.{i}.convs2.{j}.1.weight_v']
                     a_						     =			checkpoint[F'blocks.{i}.convs2.{j}.1.bias']

       a_						     =			checkpoint['output_conv.1.weight_g']
       a_						     =			checkpoint['output_conv.1.weight_v']
       a_						     =			checkpoint['output_conv.1.bias']

       hf_model.remove_weight_norm()


@torch.no_grad()
def       __UpperCamelCase					(      lowercase_       :  List[Any]   ,       lowercase_       :  int   ,       lowercase_       :  Optional[Any]   ,       lowercase_       :  List[str]=None   ,       lowercase_       :  Tuple=None   ,       ):
       """simple docstring"""




       if config_path is not None:
              a_						     =			SpeechTaHifiGanConfig.from_pretrained(lowercase_   )
       else:
              a_						     =			SpeechTaHifiGanConfig()

       a_						     =			SpeechTaHifiGan(lowercase_   )

       a_						     =			torch.load(lowercase_   )
       load_weights(orig_checkpoint['model']['generator']   ,       lowercase_   ,       lowercase_   )

       a_						     =			np.load(lowercase_   )
       a_						     =			stats[0].reshape(-1   )
       a_						     =			stats[1].reshape(-1   )
       a_						     =			torch.from_numpy(lowercase_   ).float()
       a_						     =			torch.from_numpy(lowercase_   ).float()

       model.save_pretrained(lowercase_   )

       if repo_id:
              print('Pushing to the hub...'   )
              model.push_to_hub(lowercase_   )


if __name__ == "__main__":
  __lowerCAmelCase        =							argparse.ArgumentParser()
  parser.add_argument("--checkpoint_path", required=True, default=None, type=str, help="Path to original checkpoint")
  parser.add_argument("--stats_path", required=True, default=None, type=str, help="Path to stats.npy file")
  parser.add_argument("--config_path", default=None, type=str, help="Path to hf config.json of model to convert")
  parser.add_argument(
      "--pytorch_dump_folder_path", required=True, default=None, type=str, help="Path to the output PyTorch model."
  )
  parser.add_argument(
      "--push_to_hub", default=None, type=str, help="Where to upload the converted model on the 🤗 hub."
  )

  __lowerCAmelCase        =							parser.parse_args()
  convert_hifigan_checkpoint(
      args.checkpoint_path,
      args.stats_path,
      args.pytorch_dump_folder_path,
      args.config_path,
      args.push_to_hub,
  )




